SoroushVahidi/scidocs huggingface dataset:
UNDERSTANDING THE EFFECT OF INTERDEPENDENCY AND VULNERABILITY ON THE PERFORMANCE OF CIVIL INFRASTRUCTURE
(2013)

Vulnerability is a measure ofthe extent to which a community, structure, service s or geographic area is likely to be damaged or disrupted by the im pact of particular hazard. Current asset management practices focuses on studying factors that affect p rformance of isolated infrastructure networks and model a set of actions to control the expected performanc e of these networks. This approach ignores the underlying spatial and functional interdependencies among these infrastructure networks and their vulnerability. The purpose of this paper is to intr oduce a new method that recognizes the effect of sp atial and functional interdependencies on vulnerability r ating of water, sewer and road networks. The propos ed method consists of: 1) risk assessment model, 2) in terdependency assessment model and 3) vulnerability assessment model. The risk model is composed of two modules: 1) water and sewer risk module and 2) road infrastructure module. The water and sewer ris k module will cluster these assets into three risk categories based on environmental, social, operatio nal and economical factors. The road infrastructure module will cluster road assets into three risk cat egories by using rational factorial technique based on road type, serviceability index, traffic load, and freez and thaw index. The interdependency model will de ploy the risk ranking to perform geospatial analysis in ArcGIS which results in determining the interdepend t layers of waters, sewers and roads. The vulnerabili ty model will deploy fuzzy neural networks techniqu e to determine the vulnerability rating based on spatial and functional interdependencies. The fuzzy neural networks are utilized to overcome the lack of histo r cal data and incorporate experts’ preferences for establishing the knowledge base for vulnerability a ssessment. The expected contribution of this framew ork is to aid decision makers in understanding the inte rdependencies between civil infrastructure assets a nd to which extent such interdependencies can compromise as ts performance.
PREDICTING HELPFUL POSTS IN OPEN-ENDED DISCUSSION FORUMS: A NEURAL ARCHITECTURE
(2018)

Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread, to aid users comprehend long running discussion threads, which often contain repetitive or even irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on five different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.
Process reengineering in the public sector : learning some private sector lessons
(2003)

The possible applicability of business process reengineering (BPR) to organisations in the public sector is explored through analysis of the central issues in BPR and the emerging experience of organisations which have recently implemented it. In particular, the paper suggests that success of reengineering may depend critically on the strategic capability of the organisation prior to undertaking the effort. For that reason well-performing organisations are more likely to improve performance by means of BPR than are weak ones. Yet, in the public sector, it tends to be badly performing agencies which are most encouraged to undertake BPR. Knowing and understanding the reasons for success or failure of BPR in private organisations can prepare public sector managers for undertaking the effort, but each reengineering initiative must be tailored to the specific needs and circumstances of the individual agency. Public sector managers should use the widest possible definition of 'value' when analysing value-added in process reengineering and should be especially sensitive to the way in which 'value' in the public sector is differently interpreted by major stakeholders. During this learning process, public sector agencies would be well advised to be conservative in estimating gains from BPR. © 1997 Elsevier Science Ltd
An Efficient and Secure Automotive Wireless Software Update Framework
(2018)

Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.
DeepWear: a Case Study of Collaborative Design between Human and Artificial Intelligence
(2018)

Deep neural networks (DNNs) applications are now increasingly pervasive and powerful. However, fashion designers are lagging behind in leveraging this increasingly common technology. DNNs are not yet a standard part of fashion de sign practice, either clothes patterns or prototyping tools. In this paper, we present DeepWear, a method using deep convolutional generative adversarial networks for clothes design. The DNNs learn the feature of specific brand clothes and generate images then patterns instructed from the images are made, and an author creates clothes based on that. We evaluated this system by evaluating the credibility of the actual sold clothes on market with our clothes. As the result, we found it is possible to make clothes look like actual products from the generated images. Our findings have implications for collaborative design between machine and human intelligence.
IIT ( BHU ) : System Description for LSDSem ’ 17 Shared Task
(2017)

This paper describes an ensemble system submitted as part of the LSDSem Shared Task 2017 the Story Cloze Test. The main conclusion from our results is that an approach based on semantic similarity alone may not be enough for this task. We test various approaches and compare them with two ensemble systems. One is based on voting and the other on logistic regression based classifier. Our final system is able to outperform the previous state of the art for the Story Cloze test. Another very interesting observation is the performance of sentiment based approach which works almost as well on its own as our final en-
Digital forensic framework using feedback and case history keeper
(2015)

Cyber crime investigation is the integration of two technologies named theoretical methodology and second practical tools. First is the theoretical digital forensic methodology that encompasses the steps to investigate the cyber crime. And second technology is the practically development of the digital forensic tool which sequentially and systematically analyze digital devices to extract the evidence to prove the crime. This paper explores the development of digital forensic framework, combine the advantages of past twenty five forensic models and generate a algorithm to create a new digital forensic model. The proposed model provides the following advantages, a standardized method for investigation, the theory of model can be directly convert into tool, a history lookup facility, cost and time minimization, applicable to any type of digital crime investigation.
Toward Adversarial Online Learning and the Science of Deceptive Machines
(2015)

Intelligent systems rely on pattern recognition and signaturebased approaches for a wide range of sensors enhancing situational awareness. For example, autonomous systems depend on environmental sensors to perform their tasks and secure systems depend on anomaly detection methods. The availability of large amount of data requires the processing of data in a “streaming” fashion with online algorithms. Yet, just as online learning can enhance adaptability to a non-stationary environment, it introduces vulnerabilities that can be manipulated by adversaries to achieve their goals while evading detection. Although human intelligence might have evolved from social interactions, machine intelligence has evolved as a human intelligence artifact and been kept isolated to avoid ethical dilemmas. As our adversaries become sophisticated, it might be time to revisit this question and examine how we can combine online learning and reasoning leading to the science of deceptive and counter-deceptive machines.
Adaptive Multimodal Fusion by Uncertainty Compensation With Application to Audiovisual Speech Recognition
(2008)

While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact in pattern recognition tasks has received relatively little attention to date. In this paper, we explicitly take feature measurement uncertainty into account and show how multimodal classification and learning rules should be adjusted to compensate for its effects. Our approach is particularly fruitful in multimodal fusion scenarios, such as audiovisual speech recognition, where multiple streams of complementary time-evolving features are integrated. For such applications, provided that the measurement noise uncertainty for each feature stream can be estimated, the proposed framework leads to highly adaptive multimodal fusion rules which are easy and efficient to implement. Our technique is widely applicable and can be transparently integrated with either synchronous or asynchronous multimodal sequence integration architectures. We further show that multimodal fusion methods relying on stream weights can naturally emerge from our scheme under certain assumptions; this connection provides valuable insights into the adaptivity properties of our multimodal uncertainty compensation approach. We show how these ideas can be practically applied for audiovisual speech recognition. In this context, we propose improved techniques for person-independent visual feature extraction and uncertainty estimation with active appearance models, and also discuss how enhanced audio features along with their uncertainty estimates can be effectively computed. We demonstrate the efficacy of our approach in audiovisual speech recognition experiments on the CUAVE database using either synchronous or asynchronous multimodal integration models.
Distributed Auction Algorithms for the Assignment Problem with Partial Information
(2010)

operations centers (MOC), in which multiple DMs with partial information and partial control over assets are involved in the development of operational level plans. The MOC emphasizes standardized processes and methods, centralized assessment and guidance, networked distributed planning capabilities, and decentralized execution for assessing, planning and executing missions across a range of military operations  Abstract—Task-asset assignment is a fundamental problem paradigm in a wide variety of applications. Typical problem setting involves a single decision maker (DM) who has complete knowledge of the weight (reward, benefit, accuracy) matrix and who can control any of the assets to execute the tasks. Motivated by planning problems arising in distributed organizations, this paper introduces a novel variation of the assignment problem, wherein there are multiple DMs and each DM knows only a part of the weight matrix and/or controls a subset of the assets. We extend the auction algorithm to such realistic settings with various partial information structures using a blackboard coordination structure. We show that by communicating the bid, the best and the second best profits among DMs and with a coordinator, the DMs can reconstruct the centralized assignment solution. The auction setup provides a nice analytical framework for formalizing how team members build internal models of other DMs and achieve team cohesiveness over time. [1]. In this vein, we are developing analytical and computational models for multi-level coordinated mission planning and monitoring processes associated with MOCs, so that they can function effectively in highly dynamic, asymmetric, and unpredictable mission environments. Two key problem areas are: 1) realistic modeling of multi-level coordination structures that link tactical, operational and strategic levels of decision making; and 2) collaborative planning with partial information and partial control of assets. In the collaborating planning problem, each DM “owns” a set of assets and is responsible for planning certain tasks. Each task is characterized by a vector of resource requirements, while each asset is characterized by a vector of resource capabilities (see Fig. 1). Multiple assets (from the same DM or multiple DMs) may be required to process a task. The degree of match between the task-resource requirement vector and asset-resource capability vector determines the accuracy of task execution. In addition, the elements of
A Survey of First-Order Probabilistic Models
(2008)

There has been a long standing division in Artificial Intelligence between logical and probabilistic reasoning approaches. While probabilistic models can deal well with inherent uncertainty in many real-world domains, they operate on a mostly propositional level. Logic systems, on the other hand, can deal with much richer representations, especially first-order ones, but treat uncertainty only in limited ways. Therefore, an integration of these types of inference is highly desirable, and many approaches have been proposed, especially from the 1990s on. These solutions come from many different subfields and vary greatly in language, features and (when available at all) inference algorithms. Therefore their relation to each other is not always clear, as well as their semantics. In this survey, we present the main aspects of the solutions proposed and group them according to language, semantics and inference algorithm. In doing so, we draw relations between them and discuss particularly important choices and tradeoffs.
Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces
(2012)

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.
Information Extraction Supported Question Answering
(1999)

This paper discusses the use of our information extraction (IE) system, Textract, in the questionanswering (QA) track of the recently held TREC-8 tests. One of our major objectives is to examine how IE can help IR (Information Retrieval) in applications like QA. Our study shows: (i) IE can provide solid support for QA; (ii ) low-level IE like Named Entity tagging is often a necessary component in handling most types of questions; (iii ) a robust natural language shallow parser provides a structural basis for handling questions; (iv) high-level domain independent IE, i.e. extraction of multiple relationships and general events, is expected to bring about a breakthrough in QA.
Teechain: Scalable Blockchain Payments using Trusted Execution Environments
(2017)

Blockchain protocols such as Bitcoin are gaining traction for exchanging payments in a secure and decentralized manner. Their need to achieve consensus across a large number of participants, however, fundamentally limits their performance. We describe Teechain, a new off-chain payment protocol that utilizes trusted execution environments (TEEs) to perform secure, efficient and scalable fund transfers on top of a blockchain, with asynchronous blockchain access. Teechain introduces secure payment chains to route payments across multiple payment channels. Teechain mitigates failures of TEEs with two strategies: (i) backups to persistent storage and (ii) a novel variant of chain-replication. We evaluate an implementation of Teechain using Intel SGX as the TEE and the operational Bitcoin blockchain. Our prototype achieves orders of magnitude improvement in most metrics compared to existing implementations of payment channels: with replicated Teechain nodes in a trans-atlantic deployment, we measure a throughput of over 33, 000 transactions per second with 0.1 second latency.
A 2-D Electronically Steered Phased-Array Antenna With 2$\,\times\,$ 2 Elements in LC Display Technology
(2012)

For the first time, a 2-D electronically steered phased-array antenna with a liquid-crystal (LC)-based variable delay line is presented. The structure, which is designed at 17.5 GHz, consists of a 2 × 2 microstrip patch antenna array, continuously variable delay lines with a novel geometry, RF feeding, and biasing networks. The expected insertion loss of the variable delay line is less than 4 dB with a maximum differential phase shift of 300°. During the measurements, the antenna is steered by applying an appropriate dc biasing in the range of 0-15 V to the variable delay lines. It is also shown that the return loss is always better than 15 dB at the operating frequency when the antenna is steered.
Current Evidence and Opportunities for Expanding the Role of Occupational Therapy for Adults With Musculoskeletal Conditions.
(2016)

Musculoskeletal conditions are the second greatest cause of disability worldwide, and chronic musculoskeletal conditions affect nearly the same percentage of the general population as chronic circulatory and respiratory conditions combined. Moreover, people with musculoskeletal conditions experience a significant decline in independence with daily activities and occupational performance, key areas targeted by occupational therapy interventions. This special issue of the American Journal of Occupational Therapy provides comprehensive summaries of evidence for the care of common musculoskeletal conditions, highlights important implications that support evidence-informed practice, and proposes ways to advance the practice of occupational therapy to improve the lives of people with musculoskeletal conditions.
Agile Model-Driven Modernization to the Service Cloud
(2013)

Migration of legacy systems to more advanced technologies and platforms is a current issue for many software organizations. Model-Driven Modernization combined with Software as a Service delivery model is a very promising approach, which possesses a lot of advant ges, including reduced costs, automation of migration ac tivities and reuse of system functionality. However, a drawback of such an innovative modernization approach is that it lacks mature software process models to guide its adoption. Thus , a methodology for seamless execution of different mig ration and deployment activities is quite needed. On the other hand, agile development methods have been successfully adopted in various projects, which partly or thoroughly use the engineering and delivery models exploited in the modernization process. This paper presents how a pa rticular methodology for Model-Driven Modernization with deployment to the Cloud is enriched with agile tech niques to address different challenging issues. The extended agile methodology could be used by organizations which ha ve already applied agile software development as well as by organizations that plan to introduce it in their work. KeywordsCloud computing; Agile Methodology; Modeldriven Modernization; Software as a service
Organoids as an in vitro model of human development and disease
(2016)

The in vitro organoid model is a major technological breakthrough that has already been established as an essential tool in many basic biology and clinical applications. This near-physiological 3D model facilitates an accurate study of a range of in vivo biological processes including tissue renewal, stem cell/niche functions and tissue responses to drugs, mutation or damage. In this Review, we discuss the current achievements, challenges and potential applications of this technique.
Toward a Maturity Model Development Process for Information Systems ( MMDePSI )
(2013)

Maturity models are significant tools to ensure continuous improvement of systems and activities. They allow selfassessment and provide a means benchmark of these activities in relation to best practices. This article proposes the MMDePSI process for the development of new models of maturity in information systems. It begins by presenting the existing methods and approaches. Then he describes the MMDePSI process and the corresponding meta-model. Before concluding and presenting prospects of this work, it evaluates the proposed process.